Techniques for generated regular expressions are disclosed. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence. Alignment of span data structures may be performed when generating the regular expression.

Methods are presented for providing dynamic search filter suggestions that are updated and ranked based on the user filter selections. One method includes detecting a query received in a user interface (UI), calculating, by a search-candidate model, first search results, and calculating, by a suggestions model, first filter suggestions for filter categories to filter responses to the query. The suggestions model is obtained by training a machine-learning algorithm utilizing pairwise learning-to-rank modeling. The first search results and the first filter suggestions are presented in the UI. When a selection in the UI of a filter suggestion is detected, the search-candidate model calculates second search results for the filter categories based on the query and the selected filter suggestion, and the suggestions model calculates second first filter suggestions based on the query and the selected filter suggestion. The second search results and the second filter suggestions are presented in the UI.

The present disclosure provides systems and methods that generate query templates that are expressed in a generic schema-agnostic language. The query templates can be generated “from scratch” or can be automatically generated from existing queries, a process which may be referred to as “templatizing” the existing queries. As one example, generation of query templates can be performed through an iterative process that iteratively generates candidate templates over time to optimize a coverage over a set of existing queries. After generation of the schema-agnostic query templates, the systems and methods described herein can automatically translate/map the templatized queries into “concrete,” schema-specific queries that can be evaluated over specific customer schemas/datasets. In this manner, a query template for a given semantic query (e.g., “return the names of all employees”), is required to be written only once.

The present disclosure provides systems and methods that generate query templates that are expressed in a generic schema-agnostic language. The query templates can be generated “from scratch” or can be automatically generated from existing queries, a process which may be referred to as “templatizing” the existing queries. As one example, generation of query templates can be performed through an iterative process that iteratively generates candidate templates over time to optimize a coverage over a set of existing queries. After generation of the schema-agnostic query templates, the systems and methods described herein can automatically translate/map the templatized queries into “concrete,” schema-specific queries that can be evaluated over specific customer schemas/datasets. In this manner, a query template for a given semantic query (e.g., “return the names of all employees”), is required to be written only once.

A method for principal component analysis includes receiving a principal component analysis (PCA) request from a user requesting data processing hardware to perform PCA on a dataset, the dataset including a plurality of input features. The method further includes training a PCA model on the plurality of input features of the dataset. The method includes determining, using the trained PCA model, one or more principal components of the dataset. The method also includes generating, based on the plurality of input features and the one or more principal components, one or more embedded features of the dataset. The method includes returning the one or more embedded features to the user.

A distributed computing system (10) for locating product, comprising: a plurality of user devices (19); a product server (18) in communication over a network (15) with the plurality of user devices (19); and a product database (16) storing product data corresponding to a plurality of products, wherein each user device (19) is configured to: receive a user input to share an image with the product server (18); extract image data corresponding to the image from an image data source (13); and transmit the extracted image data to the product server (18), and wherein the product server (18) is configured to: in response to receiving the image data from one of the plurality of user devices (19), retrieve the image from an image source (12) based on the received image data; match the retrieved image with at least one of a plurality of products based on the product data stored in the product database (16); and transmit product data corresponding to the at least one product to the one of the plurality of user devices (19) in order to enable the one of the plurality of user devices (19) to display information relating to the at least one product.

A distributed computing system (10) for locating product, comprising: a plurality of user devices (19); a product server (18) in communication over a network (15) with the plurality of user devices (19); and a product database (16) storing product data corresponding to a plurality of products, wherein each user device (19) is configured to: receive a user input to share an image with the product server (18); extract image data corresponding to the image from an image data source (13); and transmit the extracted image data to the product server (18), and wherein the product server (18) is configured to: in response to receiving the image data from one of the plurality of user devices (19), retrieve the image from an image source (12) based on the received image data; match the retrieved image with at least one of a plurality of products based on the product data stored in the product database (16); and transmit product data corresponding to the at least one product to the one of the plurality of user devices (19) in order to enable the one of the plurality of user devices (19) to display information relating to the at least one product.

An apparatus comprising an interface and a natural language processor. The interface receives a data retrieval request formatted in a natural language and the natural language processor processes the data retrieval request. Processing the data retrieval request includes identifying database entities, database relations, or any combination thereof based words in the data retrieval request. It can also include identifying database entity criterion, database relation criterion, or any combination thereof based on words in the data retrieval request. It also includes generating a database query based on the database entities, the database relations, the database entity criterion, the database relation criterion, or any combination thereof and causing the database query to be applied to a database. Wherein, processing the data retrieval request includes grammatically tagging the data retrieval request using part-of-speech tagging techniques, e.g. grammatical type, grammatical context, semantic, or any combination thereof, and a database ontology.

Systems and methods are provided for filtering vehicle search results for an upcoming trip are described. In one example, a vehicle access platform receives a search query to view vehicles for an upcoming trip. The vehicle access platform includes an adverse outcome prediction engine and an access controller. The adverse outcome prediction engine predicts an outcome of an upcoming trip based on at least one of user information, vehicle information, or trip information. The access controller controls access, by the vehicle access platform, to available vehicles for an upcoming trip by returning to the client device filtered search results for a subset of the available vehicles that does not include prevented vehicles.

Systems, devices, and methods discussed herein are directed to generating an answer to an input query using machine reading comprehension techniques and a lattice of supported decision trees. A supported decision tree can be generated from the various decision chains (e.g., a sequence of elements comprising a premise and a decision connected by rhetorical relationships), where the nodes of the decision tree are identified from the plurality of decision chains and ordered based on a set of predefined priority rules. A lattice may include nodes that individually correspond to a respective supported decision tree. Nodes of the lattice may be identified for an input query. The passages corresponding to those nodes may be obtained and an answer for the query may be generated from the obtained passages using machine reading comprehension techniques. The generated answer may be provided in response to the query.